Discovery of Protein Biomarkers Associated to Tamoxifen Resistance

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Discovery of Protein Biomarkers Associated to Tamoxifen Resistance

Tommaso De Marchi

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The studies described in this thesis were performed at the Department of Medical Oncology, Erasmus MC Cancer Institute, Rotterdam, The Netherlands. The research described here was performed within the framework of the Erasmus Postgraduate School of Molecular Medicine.

This research project was supported by the Dutch Cancer Society (KWF).

Support for the printing was obtained by the Department of Medical Oncology, Erasmus University, Carl Zeiss BV, and New England Peptide Inc.

Cover design, printing and binding by Ridderprint BV, Ridderkerk.

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Discovery of Protein Biomarkers Associated to Tamoxifen Resistance

De ontdekking van eiwit biomerkers geassocieerd aan tamoxifen resistentie

Thesis

To obtain the degree of Doctor from the Erasmus University Rotterdam

By command of the Rector Magnificus Prof.dr. H.A.P. Pols

and in accordance with the decision of the Doctorate Board The public defense shall be held on

Thirsday 13^th October 2016, at 15:30 hours

Tommaso De Marchi

born in San Vito al tagliamento, Italy

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Doctoral committee

Supervisor: Prof.dr. J.A. Foekens Other members: Prof.dr. E.C. Zwarthoff

Prof.dr. C. Verhoef Prof.dr. C.G.J. Sweep

Co-supervisors: Dr. A. Umar

Dr.ir. J.W.M. Martens

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SPONSOR OF THE THESIS

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This thesis is dedicated to my family.

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Breast cancer is the most common malignancy among women [1]. At all tumor stages, treatment of breast cancer is determined based on the status of several markers, first of all the transcription factors estrogen receptor α (ER, ESR1 gene) and progesterone receptor (PgR, PGR gene), and the receptor tyrosine kinase human epidermal growth factor receptor-2 (HER2, ERBB2 gene). Still, many more pathological and molecular features play a role in breast cancer development, metastasis formation and therapy resistance.

1.1.1 – Intrinsic breast cancer subtypes

By using gene expression array analysis several major molecular subtypes of breast cancer, defined as intrinsic subtypes, were identified (Figure 1.1) [2]. ER positive tumors are subdivided into luminal A and B subtypes, which are characterized by the expression of estrogen regulated genes, such as GATA3, NAT1 or XBP1, and by a cell morphology similar to that of the mammary gland luminal epithelium (i.e. ducts and acini). In particular, luminal B tumors display higher expression of cell cycle genes and lower expression of luminal genes (e.g. PGR) [3]. The ER negative subgroup is further divided into tumors that show HER2 overexpression and those that are basal or normal breast-like. While the HER2 positive subgroup is associated with expression of genes such as GATA4 and GRB7, distinction between the basal and normal breast-like subtypes relates to the overexpression of genes such as CDH3 and CXCL1 (specific to the basal subtype), and PIK3R1, AKR1C1 and FACL2 (specific to the normal breast-like subtype) [4]. In addition, while basal cancer morphology resembles the basal cell layer of the mammary gland, the one of the normal breast-like subtype is more similar to healthy breast epithelium.

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Figure 1.1. Hierarchical clustering defining the intrinsic subtypes of breast cancer.

Colors are indicative of breast cancer intrinsic subtypes: luminal B (dark blue), luminal A (light blue), HER2 (red), normal-like (green), and basal (orange). Hormonal (ER and PgR) and HER2 receptor statuses are displayed in grey.

Modified from [5].

1.1.2 – Hormonal and HER2 status and treatment options

Nearly three quarters of all breast cancer cases display ER positivity, and constitute a class of relatively less aggressive tumors which generally proliferate due to ER signaling [6,7]. In ER positive breast cancers, PgR is often co-expressed with ER, though some (20-25%) ER positive tumors do not display PgR positivity. A small percentage of tumors (i.e. 3-5%) displays ER negativity and PgR positivity. The HER2 gene is found amplified in nearly 20% of all breast cancer cases and its protein HER2 can be concomitantly expressed along with ER and PgR. For both ER positive and HER2 positive breast cancer patients, treatment options include targeted therapies.

Anti-estrogens (selective estrogen receptor modulators [SERM], selective estrogen receptor degraders [SERD]) and aromatase inhibitors (AI), that respectively block ER signaling and reduce systemic estrogen levels, are used to treat ER positive malignancies, the choice of which largely depends on the menopausal status of the patient. On the other hand, trastuzumab (monoclonal antibody) and lapatinib (tyrosine kinase inhibitor) are given to ERBB2 amplified breast cancer patients [8–10]. Around 10-15% of breast cancers does not express ER, PgR or HER2 and are

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therefore being classified as triple-negative breast cancer [11]. This subgroup of patients harbors highly aggressive malignancies, with a higher relapse rate than non-triple-negative breast cancers [12]. Furthermore, in the clinic no targeted systemic treatment is currently available for these tumors, which are then treated with standard chemotherapy (Figure 1.2) [13].

Figure 1.2. Pharmacological treatment options according to breast cancer receptor status.

In accordance with breast cancers having different hormonal receptor and HER2 status, tumors of various intrinsic subtypes differ not only in gene expression patterns but, as a consequence, also display significant differences in risk of developing metastases, with basal, HER2 positive and luminal B tumors showing a significantly shorter relapse-free-survival (RFS) when compared to normal-like and luminal A subtypes [14,15].

1.2 – ER positive breast cancer 1.2.1 – Mechanism of ER-signaling

Estrogens are a class of steroidal hormones that regulate cell growth and differentiation of tissues (e.g. mammary gland, ovary, bone, and uterus) and are present in the human body as estrone, estriol and 17β-estradiol, the latter being the most abundant. These compounds bind the ER, promoting its dimerization and a conformational change in the ligand binding domain (LBD) of the receptor that allows the recruitment of transcriptional co-activators for downstream gene expression of target genes containing an ER binding site (i.e. estrogen responsive element) [16]. ER can also modulate the expression of target genes without direct binding, but by regulating transcription factors (e.g.

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AP-1 [17]) through protein-protein interactions [18]. The ligand-dependent activation of ER relies on the activation of C terminal activation function (AF) 2, located in the LBD of the receptor.

Conversely, ligand-independent activation relies on AF-1, and is activated through ER phosphorylation (described in more detail below). Subsequently to its activation (irrespective of its modality) ER associates to chromatin in concert with pioneer factors such as GATA3 and FOXA1 to activate transcription [19]. Important ER-regulated genes include PGR [20,21], and TFF1 [22,23], among others.

1.2.2 – Major endocrine therapies

The first anti-hormonal drug to be widely introduced in the clinic was tamoxifen, a SERM that acts as an antagonist to estrogens for binding to ER by recruiting transcriptional co-repressors (e.g.

NCoR) instead of co-activators [6,24], leading ultimately to tumor growth inhibition. Tamoxifen treatment in the adjuvant setting (i.e. immediately after surgical treatment of the primary tumor and radiotherapy) reduces disease recurrence and breast cancer mortality by 39% and 31%, respectively [25–27]. Furthermore, although estrogen levels vary largely between pre- and post-menopausal women, early studies showed similar survival benefits in both groups [6,25]. In addition to its action in ER positive cases, a meta-analysis study showed that a small group (i.e. 5-10%) of ER negative tumors also responded to tamoxifen treatment, though long-term survival benefits were not observed [25]. Additional studies largely confirmed that only patients with ER positive disease benefitted from tamoxifen and other endocrine agents [27]. From a clinical perspective, tamoxifen is a well-tolerated drug because its side effects are generally mild. However, long-term use of tamoxifen has been associated to an increased risk of endometrial cancer [10,28,29].

Another competitive binder of ER is fulvestrant, a SERD and pure ER antagonist that prevents its dimerization and facilitates its proteasomal degradation [30]. Due to this mechanism of action, fulvestrant treatment efficacy is unlikely to suffer from cross-resistance with other anti-estrogenic treatments, nor has it been shown to increase endometrial cancer risk [31], though further clinical confirmation in large patient cohorts still needs to be provided.

AIs (e.g. letrozole, anastrozole, exemestane) constitute a class of drugs that inhibit estrogen signaling through a different mechanism as compared to tamoxifen or fulvestrant. Inhibition of the CYP450 family enzyme aromatase leads to systemic downregulation of estrogen levels due to the blockade of testosterone conversion into estrogens. In recent years, AI-based therapy has become one of the mainstays of ER positive breast cancer treatment in post-menopausal women, in whom it

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has shown increased efficacy (30% recurrence rate reduction) when compared to tamoxifen [27,32,33]. Study reports have further shown that AIs are either equivalent or superior to tamoxifen as first-line treatment for recurrent breast cancer in post-menopausal women [34].

1.3 – General mechanisms of endocrine therapy resistance.

Tamoxifen and AIs currently constitute the two most common endocrine treatments of ER positive breast cancer. However, their effectiveness is severely reduced by the tumor`s intrinsic or acquired resistance (Figure 1.3). Although resistance occurs during all stages of the disease, it is especially a clinical problem in the recurrent setting, where nearly half of the patients already manifests resistance upon the start of treatment while the remainder develops it during therapy [35]. Several mechanisms (discussed in more detail below) have been associated with endocrine therapy resistance, such as ER gene (ESR1) mutations, epigenetic aberrations or signaling cross-talk.

1.3.1 – Estrogen receptor mutations

Somatic mutations in the ESR1 gene such A1587G, which leads to Tyr537Ser amino acid modification in the LBD of the receptor, have been shown to have a direct adverse impact on patient survival in the recurrent setting [36–39]. These mutations generally are observed in 10-30%

of all endocrine resistant recurrent breast cancers and have been linked to enhanced sensitivity to estradiol as well as to constitutive activation of transcriptional activity of ER in absence of ER agonists [36,37,40,41]. Strikingly, these mutations seem to present themselves only after exposure to one or more lines of endocrine treatment (in particular AIs) in the recurrent setting [36,42], as concluded from paired analyses of primary tumors and their metastatic therapy resistant counterparts [42,43]. Taken together, these observations support the idea that ESR1 gene mutations, especially the ones in the LBD, prevalently arise due to endocrine therapy selection of resistant clones. Functional characterization as well as therapeutic targeting of these mutants is just in its infancy. However, due to their predictive value, monitoring of these mutations through the course of endocrine therapy – namely in metastatic lesions, circulating tumor cells, or cell-free DNA (cfDNA) [38,42–44] - may help clinicians to identify and monitor resistant patients.

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1.3.2 –Dysregulation of gene expression and cross-talk mechanisms

A plethora of mechanisms can influence ER-mediated gene expression, such as changes in expression or occurrence of mutations in ER transcriptional modulators. Numerous studies have pointed out the association of the nuclear receptor co-activator AIB1 to tamoxifen therapy outcome, though it remains unclear whether it promotes therapy benefit or resistance [45–48]. Other co- activators that have been linked to tamoxifen resistance are RIP140, which regulates genes that have been linked to tamoxifen resistance [49], and SRC-1, which acts as a limiting co-factor for the transactivation of ESR1 and PGR gene expression [50]. Conversely the downregulation of ER transcriptional co-repressors, such as NCoR1, have also been shown to contribute to resistance to tamoxifen therapy [51,52]. These molecular alterations have been associated with promotion of cell cycle progression despite tamoxifen treatment, and as a consequence they possibly constitute alternative therapeutic targets [51]. In addition to this, transcription factors (e.g. GATA3) and transcriptional complex stabilizing factors (e.g. GREB1) have been shown to contribute to expression dysregulation of ER-related genes leading to endocrine resistance [19,53–56].

Recent studies have pointed out that epigenetic changes, in particular DNA hypo-/hypermethylation at CpG islands and histone modifications (e.g. methylation at lysine residues) [57–60], can also affect outcome to endocrine therapy. Alterations in the DNA methylation pattern has been previously associated to resistance to tamoxifen therapy, such as the hypomethylation at the promoter region of PSAT1, a gene coding for an enzyme involved in serine biosynthesis [61].

Furthermore, endocrine treatment resistance has been associated with epigenetic gene silencing such as in the case of HOXC10, a homeobox gene involved in apoptosis and cell growth inhibition [62], or the recruitment of histone-related proteins such as EZH2, a polycomb protein with histone methyltransferase properties [62,63], which has been found upregulated in many breast cancers [64–66].

Several kinases of the MAPK family, such as ERK1 and ERK3, have been shown to phosphorylate ER (e.g. at Ser-118), prompting ligand-independent activation of the receptor, and altering response to ER agonists and antagonists [67–69]. Furthermore, the expression of HER2, EGFR or IGFR can ultimately induce phosphorylation of ER and AIB1 through cross-talk mechanisms, which have been shown to empower estrogen signaling and induce resistance to tamoxifen [70–73].

Phosphoinositide 3 kinase (PI3K) and protein kinase B (PKB; also known as Akt) also play a role in activation of ER-related transcription: these kinases activate the receptor in the absence of estrogens through phosphorylation of the AF-1 (PI3K) and AF-2 (PI3K and PKB) domains of the receptor

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[74]. Furthermore, activating phosphorylations of the ER can be enacted by other kinases, such as c- Jun terminal kinase or p38 [75–77].

Signaling pathway cross-talk mechanisms have also been associated to the expression of long non- coding (lnc) RNAs, such as BCAR4 (co-expressed with ERBB2) [78–80], which have been shown to promote resistance to endocrine therapies. Taken together, these molecules may not only be used as biomarkers of outcome to endocrine therapy, but could also constitute alternative therapeutic targets in endocrine therapy resistant patients.

Figure 1.3. Schematic representation of major endocrine resistance mechanisms.

1.4 – Prediction of tamoxifen therapy resistance 1.4.1 – Traditional clinical predictive factors

Several clinical and molecular markers have been associated with response to tamoxifen treatment, both in the adjuvant and in the recurrent setting. Although such information is not always easily

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defined in the clinic, it has been established that post-menopausal women do benefit less from tamoxifen therapy in the adjuvant setting and their regimen is therefore switched to AI [81].

Among the molecular markers, ER and PgR levels are so far the best clinical markers predictive of tamoxifen outcome [82,83]. In addition to this, mitotic index and Ki-67 levels are also in use and have been associated to poor prognosis [3,84–86].

1.4.2 –Biomarkers of tamoxifen resistance

With the introduction of gene expression analysis (e.g. hybridization chips) and new techniques to study gene expression in vitro (e.g. RNA silencing), new methods of biomarker discovery and investigation have become available. While classifiers predictive of recurrence for ER positive breast cancer (e.g. Mammaprint® [87]) found gradual introduction into clinical practice, tamoxifen resistance predictive signatures still needed development [88,89]. Subsequently, either single genes (e.g. BCAR4) or entire gene lists (e.g. 44- and 76-gene signatures) were used as classifiers of tamoxifen resistance in the adjuvant and/or recurrent settings [64,79,80,90,91]. Furthermore, with the advent of epigenomics and the dissection of DNA transcription mechanisms, new perspectives have been added to the investigation of tamoxifen resistance. Signatures developed by analysis of ER and histone proteins binding sites (e.g. H3K4me3) not only significantly predicted patient outcome in AI and tamoxifen treated patients, but outperformed previous gene expression classifiers [49,92].

1.5 – The proteomic approach

1.5.1 – Mass spectrometry: a cutting-edge technique for the study of proteins

With the improvement of chromatographic and ion separation techniques, mass spectrometry (MS) has become one of the most advanced techniques to address biological and clinical issues, being able to identify and quantify > 10,000 protein species in a single biological specimen, rendering this technique a robust additional tool complementary to gene expression analysis [93–95].

Furthermore, the elucidation of the human proteome showed the full capabilities of the proteomic approach in analyzing the wide dynamic range of protein expression present throughout different tissues [96,97]. In addition, with the possibility to detect post-translational modifications (PTMs), a second functional layer of information can be provided [98,99].

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1.5.2 – Common quantitation methods in proteomics

In MS data analysis, protein identification is the first step, which can be followed by quantitation.

The latter can be achieved through a plethora of methods (Figure 1.4). Chemical labeling techniques (e.g. isotope tags for relative and absolute quantitation [iTRAQ], tandem mass tags [TMT]) constitute one of the most common approaches for proteomic analyses of large sample sets, offer the possibility to accurately quantify proteins in a robust and reproducible way [100,101]. However, these techniques require relatively high amounts of sample material, and pre-labeling protein enrichment is impractical when working with small amounts of sample material. Furthermore, peptide quantitation through these methods may suffer from interference derived from co-eluting peptides of a similar mass, though this can be minimized by employment of fractionation techniques. In this perspective, an iTRAQ or TMT experiment would rely on several sample preparation steps, which would be time-consuming and susceptible to variation in protein quantitation due to the extensive sample manipulation [102]. In this perspective, algorithms that enable quantification without previous labeling are becoming attractive alternatives. Label-free quantification (LFQ), which is provided within the MaxQuant environment for example, does not rely on sample pre-processing, and protein levels are computationally derived from the intensity of their peptide-spectrum matches (PSMs), which are compared between samples. Another approach consists of counting fragmentation spectra belonging to a protein-specific peptide, the so-called spectral count [103]. While the first approach uses peptide extracted ion chromatograms and integrates them through the chromatographic run (in the time scale), the second counts peptide fragmentation spectra, which are based on MS2 scans, and compares them for every peptide to achieve relative quantitation [102]. Despite the fact that label-free methods provide less accurate quantification compared to chemical labeling methods, the number of experiments that can be compared is virtually unlimited, rendering such approach well suited for the analysis of large sample cohorts with two or more experimental conditions. Furthermore, LFQ has proven to identify a relatively higher amount of proteins when compared to labeling strategies, thus potentially providing a higher analytical depth. Recent studies have demonstrated that combination of tissue enrichment strategies with sample fractionation and LFQ allows the quantification of thousands of proteins, especially in the low abundance range [102,104–107]. Furthermore, with the continuous improvements in LFQ softwares (e.g. MaxQuant), protein quantification has become progressively more accurate [108–111].

While these methods are generally used in global proteomic studies, other quantitation strategies are used to determine highly accurate abundances of target peptides/proteins [102,112]. These targeted

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approaches are generally more suited for biomarker verification and clinical assay development strategies, due to the fact they provide precise quantitation of (theoretically) hundreds of proteotypic peptides in a sensitive and reproducible way. Protein quantitation methods for these targeted assays, such as selected reaction monitoring (SRM), comprise label-free and chemical labeling approaches, as well as isotopically labeled standard spikes. Since isotopically labeled versions of target analytes are used as internal standards, they retain the same physical and chemical properties of their endogenous counterparts, minimizing interference derived from various sources (e.g. co-eluting species) [113]. Furthermore, when compared to immuno-assays (e.g. enzyme-linked immuno- sorbent assay [ELISA]), SRM MS provides comparable selectivity and lower development costs [114,115].

Figure 1.4. Most common protein quantitation methods used in MS-based proteomics.

Blue and yellow boxes represent different experimental conditions, while dashed lines indicate steps at which experimental variation can occur. Modified from [102].

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1.5.3 – Proteomic approaches for biomarkers

Proteomic approaches for the development and validation of clinically useful biomarkers are diverse, though they can be summarized into two main categories: global (or shotgun) and targeted proteomic analyses. While in the former approach the whole (or a subset of the) dynamic range of proteins expressed in a biological sample is measured, the latter employs previous biological knowledge to specifically identify and accurately quantify a putative marker or a subset of proteins (e.g. N-glycosylated proteins). In the biomarker discovery phase, statistical methods are applied to define significant differences in (modified and/or unmodified) protein levels between two or more experimental or biological/clinical conditions (t-test, ANOVA, etc.) [116]. These differentially expressed proteins constitute a list of putative biomarkers from which a predictor can be developed, which are then verified in an independent set of samples (i.e. validation set). Both global and targeted approaches are used in biomarker discovery studies, though a general consensus remains:

while global proteomic analysis is generally used to identify putative biomarkers, ELISA assays or targeted MS approaches are then used to provide accurate and quantitative measurements, which provide biological and technical validations as well as a more clinically feasible assay [105,111,117,118].

1.5.4 – Phosphoproteomics

The most common post-translational modification, dysregulation of protein phosphorylation, has been reported as one of the critical factors associated to cancer development, metastasis formation, and therapy resistance (e.g. ERBB2, EGFR) [119,120]. On the technical side, the study of phosphorylated proteins has so extensively evolved through the development of multiplexable techniques (e.g. reverse-phase protein arrays, peptide arrays, MS), that assessment of thousands of phosphorylation events is nowadays standard practice in many laboratories [121]. On the biological side, not only the analysis of phosphorylated proteins enables the identification of dysregulated (e.g.

hyper-activated, mutated) signaling pathways in diseases such as cancer (e.g. effects of BRAF mutations), but also provide clarifying information on drug on- and off-target effects, as well as new pharmacologically targetable biomarkers (e.g. kinase inhibitors) [122–125]. Measurement of post-translational modification, though, come at the cost of increasing quantitation variation due to the additional purification step required (i.e. increased sample manipulation). Furthermore, while modified peptides are less ionizable compared to their unmodified counterparts, modifications are also more labile, impacting overall sensitivity of the MS measurement [126]. Alternative ionization

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techniques (e.g. combined electron transfer and collision induced dissociation EThcD) have been developed to solve this issue in the field of phosphoproteomics [127], though the laborious sample preparation impacts negatively on the analysis of large cohorts of clinical specimens.

1.5.5 - Glycoproteomics

While phosphorylation changes elucidate the status of kinases in a given biological condition, glycosylation patterns provide additional information on cell-to-cell adhesion or protein folding [128,129]. Furthermore, glycosylated protein function may also change depending on the nature or the position of the attached group. Elucidation of the function of glycosylated proteins in complex diseases has pointed out that alteration of glycosylation pathways are hallmarks of cancer progression and metastasis [130,131]. Furthermore, glycosylated proteins are also secreted in the bloodstream and may constitute viable disease biomarkers for diagnostic purposes. Despite this, extensive glyco-proteomic analyses remain challenging due to extensive sample preparation, lability of modifications, and difficulties in glycosylated peptide spectra annotations [117,132].

1.5.6- Protein markers of tamoxifen resistance

In recent years, several studies reported global MS-derived biomarkers for tamoxifen resistance in the adjuvant setting. Proteomic studies in both ER positive breast cancer cell lines and patient- derived tumors showed that senescence inducing (i.e. RARA) and proliferation-related (i.e. CAPS) proteins were associated to short RFS and poor outcome to adjuvant tamoxifen therapy [133,134].

Other studies investigated changes in serum protein levels between tumors either responsive or resistant to tamoxifen treatment (e.g. Apo-lipoprotein E) [135].

Only one study investigated tamoxifen therapy resistance in the recurrent setting through MS, in which a panel of 100 proteins was derived from the analysis of 51 ER positive breast cancers.

Despite these initial findings, only CD147 (or EMMPRIN; i.e. the most significant protein) was validated in an independent sample set through immunohistochemistry (IHC) [136].

Taken together these studies paved the way for clinical breast cancer proteomics. However, each of them suffered from either relatively small sample numbers or tumor heterogeneity unrepresentative (i.e. cell lines) discovery sets. Furthermore, most studies were small sized or lacked of validation

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cohorts, necessitating large independent verification sets prior to biomarker introduction into a clinical setting.

1.5.7 – Tissue proteomic workflow for biomarker discovery

In the search for prognostic and predictive cancer biomarkers, both genomic and proteomic analyses are hampered by the presence of multiple cell types in primary tissues, which can alter accurate quantitation due to signals derived from tumor-surrounding tissues (e.g. stromal cells, adipocytes, leucocytes) [137–139]. To overcome this issue, cell population enrichment techniques, such as LCM, have proven to be powerful tools in elucidating tumor biology and approaches to biomarker discovery [140–142]. Despite the high purity of sample material derived from cell enrichment procedures, only minute amounts of tissue can be derived in a time- and cost-effective manner, which can limit the number of protein identifications on the MS level. Improvements in liquid chromatography and with the next generation of high resolution MS (i.e. Orbitrap series), the amount of identified and quantified proteins from LCM material was highly improved [104].

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References:

[1] Siegel RL, Miller KD, Jemal A. Cancer Statistics, 2015. CA Cancer J Clin 2015;65:5–29.

doi:10.3322/caac.21254.

[2] Perou CM, Sørlie T, Eisen MB, van de Rijn M, Jeffrey SS, Rees C a, et al. Molecular portraits of human breast tumours. Nature 2000;406:747–52. doi:10.1038/35021093.

[3] Esposito A, Criscitiello C, Curigliano G. Highlights from the 14(th) St Gallen International Breast Cancer Conference 2015 in Vienna: Dealing with classification, prognostication, and prediction refinement to personalize the treatment of patients with early breast cancer. Ecancermedicalscience 2015;9:518. doi:10.3332/ecancer.2015.518.

[4] Sorlie T, Tibshirani R, Parker J, Hastie T, Marron JS, Nobel A, et al. Repeated observation of breast tumor subtypes in independent gene expression data sets. Proc Natl Acad Sci U S A 2003;100:8418–23.

doi:10.1073/pnas.0932692100.

[5] Smid M, Wang Y, Zhang Y, Sieuwerts AM, Yu J, Klijn JGM, et al. Subtypes of breast cancer show preferential site of relapse. Cancer Res 2008;68:3108–14. doi:10.1158/0008-5472.CAN-07-5644.

[6] Osborne CK. Tamoxifen in the treatment of breast cancer. NEJM 1998;339:1609–18.

[7] Lumachi F, Brunello a, Maruzzo M, Basso U, Basso SMM. Treatment of estrogen receptor-positive breast cancer. Curr Med Chem 2013;20:596–604. doi:10.2174/092986713804999303.

[8] Burki TK. Adjuvant treatment for HER2-positive breast cancer. Lancet Oncol 2015;16:e59.

doi:10.1016/S1470-2045(15)70019-7.

[9] Larsen PB, Kümler I, Nielsen DL. A systematic review of trastuzumab and lapatinib in the treatment of women with brain metastases from HER2-positive breast cancer. Cancer Treat Rev 2013;39:720–7.

doi:10.1016/j.ctrv.2013.01.006.

[10] Schiavon G, Smith IE. Endocrine therapy for advanced/metastatic breast cancer. Hematol Oncol Clin North Am 2013;27:715–36, viii. doi:10.1016/j.hoc.2013.05.004.

[11] Foulkes W, Smith I, Reis-Filho JS. Triple-Negative Breast Cancer. NEJM 2010;363:1938–48.

[12] Dent R, Trudeau M, Pritchard KI, Hanna WM, Kahn HK, Sawka C a., et al. Triple-negative breast cancer:

Clinical features and patterns of recurrence. Clin Cancer Res 2007;13:4429–34. doi:10.1158/1078-0432.CCR-06-3045.

[13] Silver DP, Richardson AL, Eklund AC, Wang ZC, Szallasi Z, Li Q, et al. Efficacy of neoadjuvant cisplatin in triple-negative breast cancer. J Clin Oncol 2010;28:1145–53. doi:10.1200/JCO.2009.22.4725.

[14] Prat A, Perou CM. Deconstructing the molecular portraits of breast cancer. Mol Oncol 2011;5:5–23.

doi:10.1016/j.molonc.2010.11.003.

[15] Creighton CJ. The molecular profile of luminal B breast cancer. Biol Targets Ther 2012;6:289–97.

doi:10.2147/BTT.S29923.

(24)

[16] Rosenfeld MG, Glass CK. Coregulator codes of transcriptional regulation by nuclear receptors. J Biol Chem 2001;276:36865–8. doi:10.1074/jbc.R100041200.

[17] Uht RM, Webb P, Nguyen P, Price Jr Jr. RH, Valentine C, Favre H, et al. A conserved lysine in the estrogen receptor DNA binding domain regulates ligand activation profiles at AP-1 sites, possibly by controlling interactions with a modulating repressor. Nucl Recept 2004;2:2. doi:10.1186/1478-1336-2-2\r1478-1336-2-2 [pii].

[18] Nilsson S, Ma S, Treuter E, Tujague M, Thomsen J. Mechanisms of Estrogen Action. Physiol Rev 2001;81:1535–65.

[19] Theodorou V, Stark R, Menon S, Carroll JS. GATA3 acts upstream of FOXA1 in mediating ESR1 binding by shaping enhancer accessibility. Genome Res 2013;23:12–22. doi:10.1101/gr.139469.112.

[20] Ylikomi T, Bocquel MT, Berry M, Gronemeyer H, Chambon P. Cooperation of proto-signals for nuclear accumulation of estrogen and progesterone receptors. EMBO J 1992;11:3681–94.

[21] McGuire WL, Horwitz KB, Pearson OH, Segaloff A. Current status of estrogen and progesterone receptors in breast cancer. Cancer 1977;39:2934–47.

[22] Spyratos F, Andrieu C, Hacène K, Chambon P, Rio MC. pS2 and response to adjuvant hormone therapy in primary breast cancer. Br J Cancer 1994;69:394–7.

[23] Jeltsch JM, Roberts M, Schatz C, Garnier JM, Brown AM, Chambon P. Structure of the human oestrogen- responsive gene pS2. Nucleic Acids Res 1987;15:1401–14.

[24] Shang Y, Hu X, DiRenzo J, Lazar MA, Brown M. Cofactor Dynamics and Sufficiency in Estrogen Receptor–

Regulated Transcription. Cell 2000;103:843–52. doi:10.1016/S0092-8674(00)00188-4.

[25] (EBCTCG) EBCTCG. Tamoxifen for early breast cancer: an overview of the randomised trials. Lancet 1998;351:1451–67. doi:10.1016/S0140-6736(97)11423-4.

[26] (EBCTCG) EBCTCG. Effects of chemotherapy and hormonal therapy for early breast cancer on recurrence and 15-year survival: an overview of the randomised trials. Lancet 2005;365:1687–717. doi:10.1016/S0140- 6736(05)66544-0.

[27] (EBCTCG) EBCTCG. Aromatase inhibitors versus tamoxifen in early breast cancer: patient-level meta- analysis of the randomised trials. Lancet 2015;303:1341–52. doi:10.1016/S0140-6736(15)61074-1.

[28] Jordan VC. Tamoxifen: toxicities and drug resistance during the treatment and prevention of breast cancer.

Annu Rev Pharmacol Toxicol 1995;35:195–211. doi:10.1146/annurev.pa.35.040195.001211.

[29] Neven P. Tamoxifen and endometrial lesions. Lancet 1993;342:452. doi:10.1016/0140-6736(93)91590-I.

[30] Wardell SE, Marks JR, McDonnell DP. The turnover of estrogen receptor α by the selective estrogen receptor degrader (SERD) fulvestrant is a saturable process that is not required for antagonist efficacy. Biochem Pharmacol 2011;82:122–30. doi:10.1016/j.bcp.2011.03.031.

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[31] Valachis A, Mauri D, Polyzos NP, Mavroudis D, Georgoulias V, Casazza G. Fulvestrant in the treatment of advanced breast cancer: A systematic review and meta-analysis of randomized controlled trials. Crit Rev Oncol Hematol 2010;73:220–7. doi:10.1016/j.critrevonc.2009.03.006.

[32] Bonneterre J, Thurlimann B, Robertson JFR, Krzakowski M, Mauriac L, Koralewski P, et al. Anastrozole Versus Tamoxifen as First-Line Therapy for Advanced Breast Cancer in 668 Postmenopausal Women : Results of the Tamoxifen or Arimidex Randomized Group Efficacy and Tolerability Study. J Clin Oncol 2000;18:3748–57.

[33] Bonneterre J, Buzdar A, Nabholtz JM, Robertson JF, Thürlimann B, von Euler M, et al. Anastrozole is superior to tamoxifen as first-line therapy in hormone receptor positive advanced breast carcinoma. Cancer 2001;92:2247–58.

doi:10.1002/1097-0142(20011101)92:9<2247::AID-CNCR1570>3.0.CO;2-Y [pii].

[34] Cardoso F, Harbeck N, Fallowfield L, Kyriakides S, Senkus E. Locally recurrent or metastatic breast cancer:

ESMO Clinical Practice Guidelines for diagnosis, treatment and follow-up. Ann Oncol 2012;23 Suppl 7:vii11–9.

doi:10.1093/annonc/mds232.

[35] Clarke R, Tyson JJ, Dixon JM. Endocrine resistance in breast cancer - An overview and update. Mol Cell Endocrinol 2015. doi:10.1016/j.mce.2015.09.035.

[36] Toy W, Shen Y, Won H, Green B, Sakr R a, Will M, et al. ESR1 ligand-binding domain mutations in hormone-resistant breast cancer. Nat Genet 2013;45:1439–45. doi:10.1038/ng.2822.

[37] Robinson DR, Wu Y-M, Vats P, Su F, Lonigro RJ, Cao X, et al. Activating ESR1 mutations in hormone- resistant metastatic breast cancer. Nat Genet 2013;45:1446–51. doi:10.1038/ng.2823.

[38] Segal C V, Dowsett M. Estrogen receptor mutations in breast cancer - new focus on an old target. Clin Cancer Res 2014;25:1724–6. doi:10.1158/1078-0432.CCR-14-0067.

[39] Alluri PG, Speers C, Chinnaiyan AM. Estrogen receptor mutations and their role in breast cancer progression.

Breast Cancer Res 2014;16:494. doi:10.1186/s13058-014-0494-7.

[40] Li S, Shen D, Shao J, Crowder R, Liu W, Prat A, et al. Endocrine-therapy-resistant ESR1 variants revealed by genomic characterization of breast-cancer-derived xenografts. Cell Rep 2013;4:1116–30.

doi:10.1016/j.celrep.2013.08.022.

[41] Jeselsohn R, Yelensky R, Buchwalter G, Frampton G, Meric-Bernstam F, Gonzalez-Angulo AM, et al.

Emergence of constitutively active estrogen receptor-α mutations in pretreated advanced estrogen receptor-positive breast cancer. Clin Cancer Res 2014;20:1757–67. doi:10.1158/1078-0432.CCR-13-2332.

[42] Schiavon G, Hrebien S, Garcia-murillas I, Cutts RJ, Pearson A, Tarazona N, et al. Analysis of ESR1 mutation in circulating tumor DNA demonstrates evolution during therapy for metastatic breast cancer. Sci Transl Med 2015;7:1–9. doi:10.1126/scitranslmed.aac7551.

[43] Wang P, Bahreini A, Gyanchandani R, Lucas P, Hartmaier RJ, Watters RJ, et al. Sensitive detection of mono- and polyclonal ESR1 mutations in primary tumors, metastatic lesions and cell free DNA of breast cancer patients. Clin Cancer Res 2015. doi:10.1158/1078-0432.CCR-15-1534.

(26)

[44] Butler TM, Johnson-Camacho K, Peto M, Wang NJ, Macey TA, Korkola JE, et al. Exome sequencing of cell- free DNA from metastatic cancer patients identifies clinically actionable mutations distinct from primary disease. PLoS One 2015;10:1–14. doi:10.1371/journal.pone.0136407.

[45] Burandt E, Jens G, Holst F, Jänicke F, Müller V, Quaas A, et al. Prognostic relevance of AIB1 (NCoA3) amplification and overexpression in breast cancer. Breast Cancer Res Treat 2013;137:745–53. doi:10.1007/s10549-013- 2406-4.

[46] Weiner M, Skoog L, Fornander T, Nordenskjöld B, Sgroi DC, Stål O. Oestrogen receptor co-activator AIB1 is a marker of tamoxifen benefit in postmenopausal breast cancer. Ann Oncol 2013;24:1994–9.

doi:10.1093/annonc/mdt159.

[47] Alkner S, Bendahl P-O, Grabau D, Lövgren K, Stål O, Rydén L, et al. AIB1 is a predictive factor for tamoxifen response in premenopausal women. Ann Oncol 2010;21:238–44. doi:10.1093/annonc/mdp293.

[48] Osborne CK, Bardou V, Hopp T a, Chamness GC, Hilsenbeck SG, Fuqua S a W, et al. Role of the estrogen receptor coactivator AIB1 (SRC-3) and HER-2/neu in tamoxifen resistance in breast cancer. J Natl Cancer Inst 2003;95:353–61. doi:10.1093/jnci/95.5.353.

[49] Rosell M, Nevedomskaya E, Stelloo S, Nautiyal J, Poliandri A, Steel JH, et al. Complex formation and function of estrogen receptor alpha in transcription requires RIP140. Cancer Res 2014;74:5469–79. doi:10.1158/0008- 5472.CAN-13-3429.

[50] Berns EMJJ, Van Staveren IL, Klijn JGM, Foekens JA. Predictive value of SRC-1 for tamoxifen response of recurrent breast cancer. Breast Cancer Res Treat 1998;48:87–92. doi:10.1023/A:1005903226483.

[51] Keeton EK, Brown M. Cell cycle progression stimulated by tamoxifen-bound estrogen receptor-alpha and promoter-specific effects in breast cancer cells deficient in N-CoR and SMRT. Mol Endocrinol 2005;19:1543–54.

doi:10.1210/me.2004-0395.

[52] Zhang X, Jeyakumar M, Petukhov S, Bagchi MK. A nuclear receptor corepressor modulates transcriptional activity of antagonist-occupied steroid hormone receptor. Mol Endocrinol 1998;12:513–24.

doi:10.1210/mend.12.4.0089.

[53] Wilson BJ, Giguere V. Meta-analysis of human cancer microarrays reveals GATA3 is integral to the estrogen receptor alpha pathway. Mol Cancer 2008;7:49. doi:10.1186/1476-4598-7-49.

[54] Liu H, Shi J, Wilkerson ML, Lin F. Immunohistochemical evaluation of GATA3 expression in tumors and normal tissues: A useful immunomarker for breast and urothelial carcinomas. Am J Clin Pathol 2012;138:57–64.

doi:10.1309/AJCP5UAFMSA9ZQBZ.

[55] Mohammed H, D’Santos C, Serandour AA, Ali HR, Brown GD, Atkins A, et al. Endogenous purification reveals GREB1 as a key estrogen receptor regulatory factor. Cell Rep 2013;3:342–9. doi:10.1016/j.celrep.2013.01.010.

(27)

[56] Nimmrich I, Sieuwerts AM, Meijer-Van Gelder ME, Schwope I, Bolt-De Vries J, Harbeck N, et al. DNA hypermethylation of PITX2 is a marker of poor prognosis in untreated lymph node-negative hormone receptor-positive breast cancer patients. Breast Cancer Res Treat 2008;111:429–37. doi:10.1007/s10549-007-9800-8.

[57] Barrow TM, Michels KB. Epigenetic epidemiology of cancer. Biochem Biophys Res Commun 2014;455:70–

83. doi:10.1016/j.bbrc.2014.08.002.

[58] Rodríguez-Rodero S, Delgado-Álvarez E, Fernández AF, Fernández-Morera JL, Menéndez-Torre E, Fraga MF. Epigenetic alterations in endocrine-related cancer. Endocr Relat Cancer 2014;21:R319–30. doi:10.1530/ERC-13- 0070.

[59] Pathiraja TN, Stearns V, Oesterreich S. Epigenetic Regulation in Estrogen Receptor Positive Breast Cancer—

Role in Treatment Response. J Mammary Gland Biol Neoplasia 2010;15:35–47. doi:10.1007/s10911-010-9166-0.

[60] Harbeck N, Nimmrich I, Hartmann A, Ross JS, Cufer T, Grützmann R, et al. Multicenter study using paraffin- embedded tumor tissue testing PITX2 DNA methylation as a marker for outcome prediction in tamoxifen-treated, node- negative breast cancer patients. J Clin Oncol 2008;26:5036–42. doi:10.1200/JCO.2007.14.1697.

[61] Martens JWM, Nimmrich I, Koenig T, Look MP, Harbeck N, Model F, et al. Association of DNA methylation of phosphoserine aminotransferase with response to endocrine therapy in patients with recurrent breast cancer. Cancer Res 2005;65:4101–7. doi:10.1158/0008-5472.CAN-05-0064.

[62] Pathiraja TN, Nayak SR, Xi Y, Jiang S, Garee JP, Edwards DP, et al. Epigenetic reprogramming of HOXC10 in endocrine-resistant breast cancer. Sci Transl Med 2014;6:229ra41. doi:10.1126/scitranslmed.3008326.

[63] Schuettengruber B, Chourrout D, Vervoort M, Leblanc B, Cavalli G. Genome regulation by polycomb and trithorax proteins. Cell 2007;128:735–45. doi:10.1016/j.cell.2007.02.009.

[64] Jansen MPHM, Foekens JA, van Staveren IL, Dirkzwager-Kiel MM, Ritstier K, Look MP, et al. Molecular classification of tamoxifen-resistant breast carcinomas by gene expression profiling. J Clin Oncol 2005;23:732–40.

doi:10.1200/JCO.2005.05.145.

[65] Reijm EA, Jansen MPHM, Ruigrok-Ritstier K, van Staveren IL, Look MP, van Gelder MEM, et al. Decreased expression of EZH2 is associated with upregulation of ER and favorable outcome to tamoxifen in advanced breast cancer. Breast Cancer Res Treat 2011;125:387–94. doi:10.1007/s10549-010-0836-9.

[66] Reijm EA, Timmermans AM, Look MP, Meijer-van Gelder ME, Stobbe CK, van Deurzen CHM, et al. High protein expression of EZH2 is related to unfavorable outcome to tamoxifen in metastatic breast cancer. Ann Oncol 2014;25:2185–90. doi:10.1093/annonc/mdu391.

[67] Ring A, Dowsett M. Mechanisms of tamoxifen resistance. Endocr Relat Cancer 2004;11:643–58.

doi:10.1677/erc.1.00776.

[68] Li Z, Wang N, Fang J, Huang J, Tian F, Li C, et al. Role of PKC-ERK signaling in tamoxifen-induced apoptosis and tamoxifen resistance in human breast cancer cells. Oncol Rep 2012;27:1879–86.

doi:10.3892/or.2012.1728.

(28)

[69] Heckler MM, Thakor H, Schafer CC, Riggins RB. ERK/MAPK regulates ERRγ expression, transcriptional activity and receptor-mediated tamoxifen resistance in ER+ breast cancer. FEBS J 2014;281:2431–42.

doi:10.1111/febs.12797.

[70] Shou J, Massarweh S, Osborne CK, Wakeling AE, Ali S, Weiss H, et al. Mechanisms of tamoxifen resistance:

increased estrogen receptor-HER2/neu cross-talk in ER/HER2-positive breast cancer. J Natl Cancer Inst 2004;96:926–

35. doi:10.1093/jnci/djh166.

[71] Massarweh S, Osborne CK, Creighton CJ, Qin L, Tsimelzon A, Huang S, et al. Tamoxifen resistance in breast tumors is driven by growth factor receptor signaling with repression of classic estrogen receptor genomic function.

Cancer Res 2008;68:826–33. doi:10.1158/0008-5472.CAN-07-2707.

[72] Osborne CK, Coronado-Heinsohn EB, Hilsenbeck SG, McCue BL, Wakeling a E, McClelland RA, et al.

Comparison of the effects of a pure steroidal antiestrogen with those of tamoxifen in a model of human breast cancer. J Natl Cancer Inst 1995;87:746–50. doi:10.1093/jnci/87.10.746.

[73] Gee JM, Robertson JF, Gutteridge E, Ellis IO, Pinder SE, Rubini M, et al. Epidermal growth factor receptor/HER2/insulin-like growth factor receptor signalling and oestrogen receptor activity in clinical breast cancer.

Endocr Relat Cancer 2005;12 Suppl 1:S99–111. doi:10.1677/erc.1.01005.

[74] Campbell RA, Bhat-Nakshatri P, Patel NM, Constantinidou D, Ali S, Nakshatri H. Phosphatidylinositol 3- kinase/AKT-mediated activation of estrogen receptor α: A new model for anti-estrogen resistance. J Biol Chem 2001;276:9817–24. doi:10.1074/jbc.M010840200.

[75] Feng W, Webb P, Nguyen P, Liu X, Li J, Karin M, et al. Potentiation of estrogen receptor activation function 1 (AF-1) by Src/JNK through a serine 118-independent pathway. Mol Endocrinol 2001;15:32–45.

doi:10.1210/mend.15.1.0590.

[76] Lee H, Bai W. Regulation of Estrogen Receptor Nuclear Export by Ligand-Induced and p38-Mediated Receptor Phosphorylation Regulation of Estrogen Receptor Nuclear Export by Ligand-Induced and p38-Mediated Receptor Phosphorylation. Mol Cell Biol 2002;22:5835–45. doi:10.1128/MCB.22.16.5835.

[77] Osborne CK, Schiff R. Growth factor receptor cross-talk with estrogen receptor as a mechanism for tamoxifen resistance in breast cancer. The Breast 2003;12:362–7. doi:10.1016/S0960-9776(03)00137-1.

[78] van Agthoven T, Sieuwerts AM, Meijer-van Gelder ME, Look MP, Smid M, Veldscholte J, et al. Relevance of breast cancer antiestrogen resistance genes in human breast cancer progression and tamoxifen resistance. J Clin Oncol 2009;27:542–9. doi:10.1200/JCO.2008.17.1462.

[79] Godinho MFE, Sieuwerts AM, Look MP, Meijer D, Foekens JA, Dorssers LCJ, et al. Relevance of BCAR4 in tamoxifen resistance and tumour aggressiveness of human breast cancer. Br J Cancer 2010;103:1284–91.

doi:10.1038/sj.bjc.6605884.

[80] Godinho MFE, Wulfkuhle JD, Look MP, Sieuwerts AM, Sleijfer S, Foekens JA, et al. BCAR4 induces antioestrogen resistance but sensitises breast cancer to lapatinib. Br J Cancer 2012;107:947–55.

doi:10.1038/bjc.2012.351.

(29)

[81] De Vos FYFL, van Laarhoven HWM, Laven JSE, Themmen APN, Beex LVAM, Sweep CGJ, et al.

Menopausal status and adjuvant hormonal therapy for breast cancer patients: A practical guideline. Crit Rev Oncol Hematol 2012;84:252–60. doi:10.1016/j.critrevonc.2012.06.005.

[82] Elledge RM, Green S, Pugh R, Allred DC, Clark GM, Hill J, et al. Estrogen receptor (ER) and progesterone receptor (PgR), by ligand-binding assay compared with ER, PgR and pS2, by immuno-histochemistry in predicting response to tamoxifen in metastatic breast cancer: a Southwest Oncology Group Study. Int J Cancer 2000;89:111–7.

doi:10.1002/(SICI)1097-0215(20000320)89:2<111::AID-IJC2>3.0.CO;2-W [pii].

[83] Allred DC. Issues and updates: evaluating estrogen receptor-alpha, progesterone receptor, and HER2 in breast cancer. Mod Pathol 2010;23 Suppl 2:S52–9. doi:10.1038/modpathol.2010.55.

[84] Steck K, El-Naggar AK. Comparative flow cytometric analysis of Ki-67 and proliferating cell nuclear antigen (PCNA) in solid neoplasms. Cytometry 1994;17:258–65. doi:10.1002/cyto.990170309.

[85] Beelen K, Zwart W, Linn SC. Can predictive biomarkers in breast cancer guide adjuvant endocrine therapy?

Nat Rev Clin Oncol 2012;9:529–41. doi:10.1038/nrclinonc.2012.121.

[86] Jirstrom K. Pathology parameters and adjuvant tamoxifen response in a randomised premenopausal breast cancer trial. J Clin Pathol 2005;58:1135–42. doi:10.1136/jcp.2005.027185.

[87] van t`Veer LJ, Dai H, Vijver MJ Van De, Kooy K Van Der, Marton MJ, Witteveen AT, et al. Gene expression profiling predicts clinical outcome of breast cancer. Nature 2002;415.

[88] Cobleigh MA, Tabesh B, Bitterman P, Baker J, Cronin M, Liu ML, et al. Tumor gene expression and prognosis in breast cancer patients with 10 or more positive lymph nodes. Clin Cancer Res 2005;11:8623–31. doi:10.1158/1078- 0432.CCR-05-0735.

[89] Glas AM, Floore A, Delahaye LJMJ, Witteveen AT, Pover RCF, Bakx N, et al. Converting a breast cancer microarray signature into a high-throughput diagnostic test. BMC Genomics 2006;7. doi:10.1186/1471-2164-7-278.

[90] Zhang Y, Sieuwerts AM, McGreevy M, Casey G, Cufer T, Paradiso A, et al. The 76-gene signature defines high-risk patients that benefit from adjuvant tamoxifen therapy. Breast Cancer Res Treat 2009;116:303–9.

doi:10.1007/s10549-008-0183-2.

[91] Kok M, Koornstra RH, Mook S, Hauptmann M, Fles R, Jansen MP, et al. Additional value of the 70-gene signature and levels of ER and PR for the prediction of outcome in tamoxifen-treated ER-positive breast cancer. Breast 2012;21:769–78. doi:10.1016/j.breast.2012.04.010.

[92] Jansen MPHM, Knijnenburg T, Reijm EA, Simon I, Kerkhoven R, Droog M, et al. Hallmarks of aromatase inhibitor drug resistance revealed by epigenetic profiling in breast cancer. Cancer Res 2013;73:6632–41.

doi:10.1158/0008-5472.CAN-13-0704.

[93] Aebersold R, Mann M. Mass spectrometry-based proteomics. Nature 2003;422:198–207.

doi:10.1038/nature01511.

[94] Cox J, Mann M. Is proteomics the new genomics? Cell 2007;130:395–8. doi:10.1016/j.cell.2007.07.032.

(30)

[95] Mann M, Kulak NA, Nagaraj N, Cox J. The coming age of complete, accurate, and ubiquitous proteomes. Mol Cell 2013;49:583–90. doi:10.1016/j.molcel.2013.01.029.

[96] Kim M-S, Pinto SM, Getnet D, Nirujogi RS, Manda SS, Chaerkady R, et al. A draft map of the human proteome. Nature 2014;509:575–81. doi:10.1038/nature13302.

[97] Wilhelm M, Schlegl J, Hahne H, Gholami AM, Lieberenz M, Savitski MM, et al. Mass-spectrometry-based draft of the human proteome. Nature 2014;509:582–7. doi:10.1038/nature13319.

[98] Mann M, Jensen ON. Proteomic analysis of post-translational modifications. Nat Biotechnol 2003;21:255–61.

doi:10.1038/nbt0303-255.

[99] Jensen ON. Modification-specific proteomics: characterization of post-translational modifications by mass spectrometry. Curr Opin Chem Biol 2004;8:33–41. doi:10.1016/j.cbpa.2003.12.009.

[100] Gygi SP, Rist B, Gerber SA, Turecek F, Gelb MH, Aebersold R. Quantitative analysis of complex protein mixtures using isotope-coded affinity tags. Nat Biotechnol 1999;17:994–9. doi:10.1038/13690.

[101] Thompson A, Schäfer J, Kuhn K, Kienle S, Schwarz J, Schmidt G, et al. Tandem mass tags: a novel quantification strategy for comparative analysis of complex protein mixtures by MS/MS. Anal Chem 2003;75:1895–

904.

[102] Bantscheff M, Schirle M, Sweetman G, Rick J, Kuster B. Quantitative mass spectrometry in proteomics: a critical review. Anal Bioanal Chem 2007;389:1017–31. doi:10.1007/s00216-007-1486-6.

[103] Wang M, You J, Bemis KG, Tegeler TJ, Brown DPG. Label-free mass spectrometry-based protein quantification technologies in proteomic analysis. Briefings Funct Genomics Proteomics 2008;7:329–39.

doi:10.1093/bfgp/eln031.

[104] Wiśniewski JR, Ostasiewicz P, Mann M. High recovery FASP applied to the proteomic analysis of microdissected formalin fixed paraffin embedded cancer tissues retrieves known colon cancer markers. J Proteome Res 2011;10:3040–9. doi:10.1021/pr200019m.

[105] Liu NQ, Braakman RBH, Stingl C, Luider TM, Martens JWM, Foekens JA, et al. Proteomics pipeline for biomarker discovery of laser capture microdissected breast cancer tissue. J Mammary Gland Biol Neoplasia 2012;17:155–64. doi:10.1007/s10911-012-9252-6.

[106] Braakman RBH, Tilanus-Linthorst MMA, Liu NQ, Stingl C, Dekker LJM, Luider TM, et al. Optimized nLC- MS workflow for laser capture microdissected breast cancer tissue. J Proteomics 2012;75:2844–54.

doi:10.1016/j.jprot.2012.01.022.

[107] Liu NQ, Dekker LJM, Stingl C, Güzel C, De Marchi T, Martens JWM, et al. Quantitative proteomic analysis of microdissected breast cancer tissues: Comparison of label-free and SILAC-based quantification with shotgun, directed, and targeted MS approaches. J Proteome Res 2013;12:4627–41. doi:10.1021/pr4005794.

[108] Zhu W, Smith JW, Huang CM. Mass spectrometry-based label-free quantitative proteomics. J Biomed Biotechnol 2010;2010. doi:10.1155/2010/840518.

(31)

[109] Cox J, Mann M. MaxQuant enables high peptide identification rates, individualized p.p.b.-range mass accuracies and proteome-wide protein quantification. Nat Biotechnol 2008;26:1367–72. doi:10.1038/nbt.1511.

[110] Cox J, Neuhauser N, Michalski A, Scheltema RA, Olsen J V, Mann M. Andromeda: a peptide search engine integrated into the MaxQuant environment. J Proteome Res 2011;10:1794–805. doi:10.1021/pr101065j.

[111] Sandin M, Chawade A, Levander F. Is label-free LC-MS/MS ready for biomarker discovery? Proteomics - Clin Appl 2015;9:289–94. doi:10.1002/prca.201400202.

[112] Gerber S a, Rush J, Stemman O, Kirschner MW, Gygi SP. Absolute quantification of proteins and phosphoproteins from cell lysates by tandem MS. Proc Natl Acad Sci U S A 2003;100:6940–5.

doi:10.1073/pnas.0832254100.

[113] Harlan R, Zhang H. Targeted proteomics: a bridge between discovery and validation. Expert Rev Proteomics 2014;11:657–61. doi:10.1586/14789450.2014.976558.

[114] Wang P, Whiteaker J, Paulovich A. The evolving role of mass spectrometry in cancer biomarker discovery.

Cancer Biol Ther 2009;8:1083–94.

[115] Whiteaker JR, Zhao L, Zhang HY, Feng L-C, Piening BD, Anderson L, et al. Antibody-based enrichment of peptides on magnetic beads for mass-spectrometry-based quantification of serum biomarkers. Anal Biochem 2007;362:44–54. doi:10.1016/j.ab.2006.12.023.

[116] Rifai N, Gillette MA, Carr SA. Protein biomarker discovery and validation: the long and uncertain path to clinical utility. Nat Biotechnol 2006;24:971–83. doi:10.1038/nbt1235.

[117] Schiess R, Wollscheid B, Aebersold R. Targeted proteomic strategy for clinical biomarker discovery. Mol Oncol 2009;3:33–44. doi:10.1016/j.molonc.2008.12.001.

[118] Chung L, Baxter RC. Breast cancer biomarkers: proteomic discovery and translation to clinically relevant assays. Expert Rev Proteomics 2012;9:599–614. doi:10.1586/epr.12.62.

[119] Rexer BN, Arteaga CL. Intrinsic and Acquired Resistance to HER2-Targeted Therapies in HER2 Gene- Amplified Breast Cancer: Mechanisms and Clinical Implications. Crit Rev Oncog 2012;17:1–16.

doi:10.1615/CritRevOncog.v17.i1.20.

[120] Chong CR, Jänne PA. The quest to overcome resistance to EGFR-targeted therapies in cancer. Nat Med 2013;19:1389–400. doi:10.1038/nm.3388.

[121] Mumby M, Brekken D. Phosphoproteomics: new insights into cellular signaling. Genome Biol 2005;6:230.

doi:10.1186/gb-2005-6-9-230.

[122] Smit MA, Maddalo G, Greig K, Raaijmakers LM, Possik PA, Breukelen B Van, et al. ROCK 1 is a potential combinatorial drug target for BRAF mutant melanoma 2014:1–15.

[123] Knight ZA, Lin H, Shokat KM. Targeting the cancer kinome throuhg polypharmacology. Nat Rev Cancer 2010;10:130–7. doi:10.1038/nrc2787.Targeting.

(32)

[124] Zhang Y, Wolf-Yadlin A, Ross PL, Pappin DJ, Rush J, Lauffenburger DA, et al. Time-resolved mass spectrometry of tyrosine phosphorylation sites in the epidermal growth factor receptor signaling network reveals dynamic modules. Mol Cell Proteomics 2005;4:1240–50. doi:10.1074/mcp.M500089-MCP200.

[125] Harsha HC, Pandey A. Phosphoproteomics in cancer. Mol Oncol 2010;4:482–95.

doi:10.1016/j.molonc.2010.09.004.

[126] Roepstorff P. Mass spectrometry based proteomics, background, status and future needs. Protein Cell 2012;3:641–7. doi:10.1007/s13238-012-2079-5.

[127] Frese CK, Zhou H, Taus T, Altelaar AM, Mechtler K, Heck AJR, et al. Unambiguous phosphosite localization using electron-transfer/higher-energy collision dissociation (EThcD). J Proteome Res 2013;12:1520–5.

doi:10.1021/pr301130k.

[128] Spiro RG. Protein glycosylation: nature, distribution, enzymatic formation, and disease implications of glycopeptide bonds. Glycobiology 2002;72:43R – 56R. doi:10.1093/glycob/12.4.43R.

[129] Aebi M. N-linked protein glycosylation in the ER. Biochim Biophys Acta 2013;1833:2430–7.

doi:10.1016/j.bbamcr.2013.04.001.

[130] Pan S, Chen R, Aebersold R, Brentnall T a. Mass spectrometry based glycoproteomics-from a proteomics perspective. Mol Cell Proteomics 2011;10:R110.003251. doi:10.1074/mcp.R110.003251.

[131] Shriver Z, Raguram S, Sasisekharan R. Glycomics: a pathway to a class of new and improved therapeutics. Nat Rev Drug Discov 2004;3:863–73.

[132] Anderson NL, Polanski M, Pieper R, Gatlin T, Tirumalai RS, Conrads TP, et al. The human plasma proteome:

a nonredundant list developed by combination of four separate sources. Mol Cell Proteomics 2004;3:311–26.

doi:10.1074/mcp.M300127-MCP200.

[133] Johansson HJ, Sanchez BC, Mundt F, Forshed J, Kovacs A, Panizza E, et al. Retinoic acid receptor alpha is associated with tamoxifen resistance in breast cancer. Nat Commun 2013;4:2175. doi:10.1038/ncomms3175.

[134] Johansson HJ, Sanchez BC, Forshed J, Stål O, Fohlin H, Lewensohn R, et al. Proteomics profiling identify CAPS as a potential predictive marker of tamoxifen resistance in estrogen receptor positive breast cancer. Clin Proteomics 2015;12:4–13. doi:10.1186/s12014-015-9080-y.

[135] Majidzadeh-A K, Gharechahi J. Plasma proteomics analysis of tamoxifen resistance in breast cancer. Med Oncol 2013;30:753. doi:10.1007/s12032-013-0753-y.

[136] Umar A, Kang H, Timmermans AM, Look MP, Meijer-van Gelder ME, den Bakker M a, et al. Identification of a putative protein profile associated with tamoxifen therapy resistance in breast cancer. Mol Cell Proteomics 2009;8:1278–94. doi:10.1074/mcp.M800493-MCP200.

[137] Jr. DJJ, Rodriguez-Canales J, Mukherjee S, Prieto DA, C.Hanson J, Emmert-Buck M, et al. Approaching solid tumor heterogeneity on a cellular basis by tissue proteomics using laser capture microdissection and biological mass spectrometry. J Proteome Res 2010;8:2310–8. doi:10.1021/pr8009403.Approaching.

(33)

[138] Longo D. Tumor Heterogeneity and Personalized Medicine. NEJM 2012;366:956–7.

[139] Kondo T. Inconvenient truth: cancer biomarker development by using proteomics. Biochim Biophys Acta 2014;1844:861–5. doi:10.1016/j.bbapap.2013.07.009.

[140] Yang F, Foekens JA, Yu J, Sieuwerts a M, Timmermans M, Klijn JGM, et al. Laser microdissection and microarray analysis of breast tumors reveal ER-alpha related genes and pathways. Oncogene 2006;25:1413–9.

doi:10.1038/sj.onc.1209165.

[141] Emmert-buck MR, Bonner RF, Smith PD, Chuaqui RF, Zhuang Z, Goldstein SR, et al. Laser Capture Microdissection. Science (80- ) 1996;274:8–11.

[142] Xu BJ. Combining laser capture microdissection and proteomics: methodologies and clinical applications.

Proteomics Clin Appl 2010;4:116–23. doi:10.1002/prca.200900138.

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Discovery of Protein Biomarkers Associated to Tamoxifen Resistance

This thesis is dedicated to my family.

CONTENTS